Hierarchical Aggregation of Uncertain Sensor Data for M2M Wireless Sensor Network Using Reinforcement Learning
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- 발행기관 HINDAWI PUBLISHING CORPORATION
- 발행년도 2014
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000091694
- 본문언어 영어
- Published As http://dx.doi.org/10.1155/2014/535707
초록/요약
The communication among heterogeneous embedded devices could lead to correctness problems in M2M environment. Sometimes, it is not easy to classify the data because they may provide wrong or uncertain information. The data from these devices should be gathered in a safe, efficient, and right manner without the help of server or human intervention; even the low-level information from each device causes interoperability problems. This data gathering or data fusion process is very important because the data mapping result could be understood as totally different situation and hence cause different reaction, feedback, and controls. In this paper, we propose a hierarchical aggregation for uncertain sensor data using reinforcement learning to get correct and efficient data gathering result for reliable wireless sensor network. In our proposal, we add a new category for uncertain data and classify them through reinforcement learning using hierarchical subcategories. By adopting our proposed aggregation, false classification caused by uncertain data can be decreased and the correctness of data gathering can be enhanced.
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